AI broke code review. What about knowledge sharing?

AI-generated code is increasing review volumes beyond what engineers can realistically inspect line by line, threatening code review’s traditional role in teaching, architectural discussion, and shared ownership. While automated tools can handle many bug, standards, and verification tasks, teams risk accumulating “cognitive debt” when members no longer understand a system’s purpose, assumptions, tradeoffs, or ownership. The proposed response is to shift human review upstream toward intent-driven verification, in which engineers document scope, acceptance criteria, architectural choices, constraints, and explicitly excluded work before implementation is submitted. Reviewers can then focus on whether a change addresses the right problem under the right constraints rather than examining every generated line, while authors retain the mental models needed to direct and challenge AI agents.

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